This paper analyzes the deficiencies of current grid trust systems based on PKI (Public Key Infrastructure), ambiguity certificate principal information, and complicated identification process. Inspired by biologic gene technique, we propose a novel grid trust model based on Family Gene (FG) model. The model answers the existing questions in tradition trust model by adopting the technology of family gene. The concepts and formal definitions of Family Gene in the grid trust security domains are given. Then, the mathematical models of Family Gene are established. Our theoretical analysis and experimental results show that the model is a good solution to grid trust domain.

This paper proposes a novel grid intrusion detection model based on immune agent (GIDIA), and gives the concepts and formal definitions of self, nonself, antibody, antigen, agent and match algorithm in the grid security domain. Then, the mathematical models of mature MoA (mature monitoring agent), and dynamic memory MoA (memory monitoring agent) survival are established. Besides, effects of the important parameter Τ in the model of mature MoA on system performance are showed. Our theoretical analysis and experimental results show that the model that has higher detection efficiency and steadier detection performance than the current models is a good solution to grid intrusion detection.

Building on the concepts and the formal definitions of self, nonself, antigen, and detector introduced in the research of network intrusion detection, the dynamic evolution models and the corresponding recursive equations of self, antigen, immune-tolerance, lifecycle of mature detectors, and immune memory are presented. Following that, an immune-based model, referred to as AIBM, for dynamic intrusion detection is developed. Simulation results show that the proposed model has several desirable features including self-learning, self-adaption and diversity, thus providing a effective solution for network intrusion detection.